1. Field of the Invention
The field of the invention relates to computerized medical diagnostic systems. More particularly, embodiments of the present invention relate to a computerized system for time-based diagnosis of a patient""s medical complaint by use of dynamic data structures.
2. Description of the Related Technology
Health care costs currently represent a significant portion of the United States Gross National Product and are generally rising faster than any other component of the Consumer Price Index. Moreover, usually because of an inability to pay for medical services, many people are deprived of access to even the most basic medical care and information.
Many people delay in obtaining, or are prevented from seeking, medical attention because of cost, time constraints, or inconvenience. If the public had universal, unrestricted, and easy access to medical information, many diseases could be prevented. Likewise, the early detection and treatment of numerous diseases could keep many patients from reaching the advanced stages of illness, the treatment of which is a significant part of the financial burden attributed to our nation""s health care system. It is clear that the United States is facing health-related issues of enormous proportions and that present solutions are not robust.
Previous attempts at tackling the healthcare problem have involved various forms of automation. Some of these attempts have been in the form of a dial-in library of answers to medical questions. Other attempts have targeted providing doctors with computerized aids for use during a patient examination. These methods involve static procedures or algorithms. What is desired is an automated way of providing to a patient medical advice and diagnosis that is quick, efficient and accurate. Such a medical advice system should be modular to allow expansion for new types of medical problems or methods of detection.
Structure-based processing is a method of diagnosing diseases that works by arranging diseases, symptoms, and questions into a set of related disease, symptom, and question structures, such as objects or lists, in such a way that the structures can be processed to generate a dialogue with a patient. Each question to the patient generates one of a set of defined responses, and each response generates one of a set of defined questions. This establishes a dialogue that elicits symptoms from the patient. The symptoms are processed and weighted to rule diseases in or out. The set of ruled-in diseases establishes the diagnosis. A structure-based processing system organizes medical knowledge into formal structures and then executes those structures on a structure engine, such as a list-based engine, to automatically select the next question. The responses to the questions lead to more questions and ultimately to a diagnosis.
In one aspect of the present invention there is a method of automatically diagnosing a medical condition by use of a predicted timeline of symptoms, the method comprising accessing a plurality of timelines which are each representative of a typical pattern of a disease, automatically asking one or more questions of a patient so as to elicit a chief complaint, automatically receiving answers from the patient in response to the questions, automatically identifying a disease corresponding to the chief complaint, correlating the chief complaint to a timeline for the disease, automatically asking one or more questions to elicit the onset time of a symptom on the timeline for the disease, adding an incremental weight to a cumulative score for the disease if the symptom is established, and establishing the diagnosis when the cumulative score exceeds a predetermined threshold.
In another aspect of the present invention there is a method of automatically diagnosing a medical condition of a patient by use of a predicted timeline of symptoms, the method comprising generating a plurality of timelines which are each representative of a typical course of a disease via a characteristic pattern of symptom attributes over time, and automatically selecting a particular disease based on a pattern of symptom attributes associated with a patient being similar to the timeline associated with the particular disease.